What is Data Governance?

Definition

Data Governance is a process for ensuring that data is acquired, stored, consumed and shared with controls based on policies considering security, privacy, permissioned access, usage and monitoring.  

Purpose

The purpose of Data Governance is designed to ensure that data is acquired, stored, used and shared responsibly, securely, transparently and effectively, including understanding the contents, users and uses. Data Governance is also required to ensure compliance to internal policies and legal requirements.

Primary Data Governance Use Cases

Data Governance is designed to ensure that data is used responsibly, securely, transparently effectively and legally, including understanding the contents, users and uses. 

• Responsibly – Developing clear policies and direction regarding how data are used with respect to privacy, security and usage; ensuring data are not used for  inappropriate / biased decisions
• Securely – Ensuring that data is used securely, including protecting data from unauthorized access and usage, deploying methods such as end-to-end encryption
• Transparently – Data is used with appropriate permissions, and that acquisition, usage and sharing are made transparent to those providing, storing, accessing and creating insights / analytics from the data 
• Effectively – Data is used to improve business performance and customer satisfaction to improve business performance in a way that complies with internal policies and laws.
• Legally – Data are used in compliance with all relevant laws, and that notification of any variance from these laws are reported in a timely manner

Key Business Benefits of Data Governance

The main benefit of Data Governance is to ensure that data is used securely, transparently, effectively and legally, thus increasing organization awareness of the importance of using data responsibly, and managing actively the role of data in terms of improving business performance, including how data are acquired, stored, used and shared.  

Common Roles and Responsibilities associated with Data Governance

Roles important to Data Governance are as follows:

• Data Owner – There needs to be a business owner who understands the business needs for data and owns how the data is acquired, transformed, accessed and used, including subsequent reporting and analysis. This to ensure accountability, actionability as well as ownership for data governance, quality and data utility. The business owner and project sponsor are responsible for reviewing and approving the data model as well as the reports and analysis that OLAP will generate. For larger, enterprise-wide insights creation and performance measurement, a governance structure should be considered to ensure cross-functional engagement and ownership for all aspects of data acquisition, modeling and usage: reporting, analysis.
• Data Governance Council – Data Governance council is a group consisting of leaders from Legal, Risk and Compliance, as well as business functions, data, analytics and technology that are responsible for establishing and monitoring data governance policies, compliance, security and activities, including overall organization participation and maturity.
• Legal, Compliance, Risk Officer – Responsible for codifying, communicating and tracking data usage, privacy, security, risk and compliance policies, laws, participation and activities. Usually one or more members of the LCR team are responsible specifically for data privacy and usage policies and procedures.   
• Chief Information / Data / Analytics / Security – Representing technology are senior leaders from overall IT as well as leads from Data, Analytics and Security.

Common Business Processes associated with Data Governance

The process for developing and deploying Data Governance is as follows:

• Set Policies – Establish policies and procedures for data governance, including data privacy, permission, access, usage, analytics and security as well as for operational monitoring, risk and compliance. Ensure that all applicable laws and regulations are well known, communicated and addressed. 
• Data Owners – Establish business owners as owners of specific data, with responsibility for managing and approving access, quality, transformation, reporting, analysis and analytics. This can be done at an enterprise level as well as functional level depending on the type of data and it’s usage 
• Data Governance Council – Establish a data governance council having leaders from business, legal, risk, compliance as well as Data, Analytics, Security and IT.
• Data Governance Tools – Ensure that data governance is managed and monitored, including via permissioned access,  development, usage and sharing. 
• Data Sharing – Ensure that all data received from and sent to 3rd parties are identified and approved, as well as managed for compliance, including security.

Common Technologies associated with Data Governance

Technologies involved with Data Governance are as follows:

• Data Catalog  – These applications make it easier to record and manage access to data, including at the source and dataset (e.g. data product) level.
• Semantic Layer – Semantic layer applications enable the development of a logical and physical data model for use by OLAP-based business intelligence and analytics applications. The Semantic Layer supports data governance by enabling management of all data used to create reports and analyses, as well as all data generated for those reports and analyses, thus enabling governance of the output / usage aspects of input data.   
• Data Governance Tools – These tools automate the management of access to and usage of data. They can also be used to manage compliance by searching across data to determine if the format and structure of the data being stored complies with policies.

Data Governance Trends and Future Outlook

Data governance is evolving into a dynamic framework that balances innovation with regulatory demands. These momentous trends will shape its future trajectory and business application:

• Automation and AI/ML – AI-driven tools automate metadata tagging, policy enforcement, and anomaly detection, reducing manual oversight while ensuring ethical standards. Explainable AI models clarify decision-making processes, aligning with regulations like the EU AI Act to prevent bias and enhance transparency.
• Cloud-Based Solutions – Hybrid cloud architectures enable scalable governance, with geo-fencing tools that enforce regional compliance across distributed data. Platforms like Snowflake and Redshift embed governance directly into storage and analytics workflows, which can dramatically control costs and optimize security.
• Real-Time Data Governance – Streaming data from IoT and edge devices requires instant validation and policy enforcement. Industries like finance and healthcare adopt systems that redact sensitive information and flag compliance issues milliseconds after data ingestion.
• Data Privacy and Compliance – Expanding regulations, including new U.S. state laws and GDPR, drive automated classification and audit trails. Privacy-enhancing technologies anonymize data during processing, simplifying adherence to cross-border requirements.
• Data Quality Management – Machine learning identifies and corrects inconsistencies, duplicates, and biases in real time. Continuous monitoring ensures datasets meet accuracy standards for AI training and operational analytics.
• Data Democratization – Self-service portals empower non-technical teams with governed access to trusted datasets. Decentralized models like a data mesh allow domain-specific ownership while maintaining enterprise-wide consistency through unified metrics layers.
• Data Governance as a Service (DaaS) – Managed solutions emerge, offering scalable policy frameworks and compliance tools via subscription models. These services reduce infrastructure burdens, particularly for mid-sized enterprises.
• Blockchain-Based Data Governance – Immutable ledgers provide auditable records of data lineage and access, enhancing trust in regulated industries. While nascent, this approach shows promise for secure, transparent governance.

The AtScale semantic layer platform bridges these trends by virtualizing governed access to hybrid cloud data. Its unified metrics ensure consistency across BI and AI tools, while real-time validation maintains compliance.

“The semantic layer supports the implementation of comprehensive data governance policies, including data stewardship, compliance, and privacy regulations,” underscores Dave Mariani, CTO and Co-Founder of AtScale. “By embedding these policies into the data management process, the semantic layer ensures that data is handled ethically and legally,” he adds. By abstracting complexity, AtScale turns fragmented governance into a cohesive strategy for scalable, ethical data use.

AtScale and Data Governance

AtScale’s semantic layer improves data governance by ensuring that there is one model describing how data is being used for business intelligence and analytics. The Semantic Layer enables development of a unified business-driven data model that defines what data can be used. This enables ease of tracking and auditing, and ensures that all aspects of how data are defined across multiple dimensions, entities, attributes and metrics, including the source data and queries made to develop output for reporting, analysis and analytics are known and tracked.